def test_laststate_update_recalculated_exo_price(self): STRATEGY_CONTEXT = { 'strategy': { 'class': StrategyMACrossTrail, 'exo_name': './mat/strategy_270225.mat', 'direction': -1, 'opt_params': [ # OptParam(name, default_value, min_value, max_value, step) OptParamArray('Direction', [-1]), OptParam('SlowMAPeriod', 20, 10, 40, 5), OptParam('FastMAPeriod', 2, 5, 20, 5), OptParam('MedianPeriod', 5, 2, 10, 1) ], }, 'swarm': { 'members_count': 3, 'ranking_class': RankerHighestReturns(return_period=1), 'rebalance_time_function': SwarmRebalance.every_friday }, } swm_full = Swarm(STRATEGY_CONTEXT) swm_full.run_swarm() swm_full.pick() swm_start = Swarm(STRATEGY_CONTEXT) swm_start.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] += 10 swm_start.strategy.data = swm_start.strategy.data.ix[:'2016-03-04'] swm_start.run_swarm() swm_start.pick() ctx = deepcopy(STRATEGY_CONTEXT) ctx['strategy']['opt_preset'] = Swarm._parse_params(swm_start.last_members_list) swm_next = Swarm(ctx) swm_next.strategy.data = swm_next.strategy.data.ix[:'2016-03-11'] swm_next.run_swarm() # Make sure that old EXO price used self.assertEqual(-2, swm_start.last_exposure) self.assertEqual(swm_full.picked_equity.ix['2016-03-04'], swm_start.picked_equity.ix['2016-03-04'] - 10*swm_start.last_exposure) # After this run swm_start._laststate_update(swm_next.strategy.data, swm_next.raw_exposure.sum(axis=1)) self.assertEqual(swm_full.picked_equity.ix['2016-03-04'], swm_start.picked_equity.ix['2016-03-04']) self.assertAlmostEqual(swm_full.picked_equity.ix['2016-03-07'], swm_start.picked_equity.ix['2016-03-07']) self.assertAlmostEqual(swm_full.picked_equity.ix['2016-03-10'], swm_start.picked_equity.ix['2016-03-10']) self.assertAlmostEqual(swm_full.picked_equity.ix['2016-03-11'], swm_start.picked_equity.ix['2016-03-11']) self.assertEqual(-3, swm_start.last_exposure)
def test_laststate_update_handle_if_swarm_composition_is_empty_after_rebalance_with_costs(self, mock_get_costs): STRATEGY_CONTEXT = { 'strategy': { 'class': StrategyMACrossTrail, 'exo_name': './mat/strategy_270225.mat', 'direction': -1, 'opt_params': [ # OptParam(name, default_value, min_value, max_value, step) OptParamArray('Direction', [-1]), OptParam('SlowMAPeriod', 20, 10, 40, 5), OptParam('FastMAPeriod', 2, 5, 20, 5), OptParam('MedianPeriod', 5, 2, 10, 1) ], }, 'swarm': { 'members_count': 3, 'ranking_class': RankerHighestReturns(return_period=1), 'rebalance_time_function': SwarmRebalance.every_friday }, } # # Mocking the CostsManagerEXOFixed.get_costs # from backtester.matlab import loaddata exo_df, info = loaddata('./mat/strategy_270225.mat') mock_get_costs.return_value = pd.DataFrame({'rollover_costs': np.zeros(len(exo_df.index)), 'transaction_costs': np.ones(len(exo_df.index)) * 10}, index=exo_df.index) swm_full = Swarm(STRATEGY_CONTEXT) swm_full.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo'] swm_full.strategy.data.loc[:, 'delta'] = 1.0 swm_full.run_swarm() swm_full.pick() swm_start = Swarm(STRATEGY_CONTEXT) swm_start.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo'] swm_start.strategy.data = swm_start.strategy.data.ix[:'2016-03-04'] swm_start.strategy.data.loc[:, 'delta'] = 1.0 swm_start.run_swarm() swm_start.pick() ctx = deepcopy(STRATEGY_CONTEXT) ctx['strategy']['opt_preset'] = Swarm._parse_params(swm_start.last_members_list) swm_next = Swarm(ctx) swm_next.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo'] swm_next.strategy.data = swm_next.strategy.data.ix[:'2016-03-11'] swm_next.strategy.data.loc[:, 'delta'] = 1.0 swm_next.run_swarm() # Make sure that old EXO price used self.assertEqual(-2, swm_start.last_exposure) self.assertEqual(swm_full.picked_equity.ix['2016-03-03'], swm_start.picked_equity.ix['2016-03-04']) # After this run with patch('warnings.warn') as mock_warn: swm_start._laststate_update(swm_next.strategy.data, swm_next.raw_exposure.sum(axis=1), swm_next.strategy.costs) self.assertTrue(mock_warn.called) dt = '2016-03-04' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-07' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-08' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-09' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-10' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-11' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
def test_laststate_update_real_with_costs(self, mock_get_costs): from backtester.matlab import loaddata STRATEGY_CONTEXT = { 'strategy': { 'class': StrategyMACrossTrail, 'exo_name': './mat/strategy_270225.mat', 'direction': -1, 'opt_params': [ # OptParam(name, default_value, min_value, max_value, step) OptParamArray('Direction', [-1]), OptParam('SlowMAPeriod', 20, 10, 40, 5), OptParam('FastMAPeriod', 2, 5, 20, 5), OptParam('MedianPeriod', 5, 2, 10, 1) ], }, 'swarm': { 'members_count': 3, 'ranking_class': RankerHighestReturns(return_period=1), 'rebalance_time_function': SwarmRebalance.every_friday }, 'costs': { 'manager': CostsManagerEXOFixed, 'context': { 'costs_options': 3.0, 'costs_futures': 3.0, } } } # # Mocking the CostsManagerEXOFixed.get_costs # exo_df, info = loaddata('./mat/strategy_270225.mat') mock_get_costs.return_value = pd.DataFrame({'rollover_costs': np.zeros(len(exo_df.index)), 'transaction_costs': np.ones(len(exo_df.index)) * 10}, index=exo_df.index) swm_full = Swarm(STRATEGY_CONTEXT) swm_full.strategy.data.loc[:, 'delta'] = 1.0 swm_full.run_swarm() swm_full.pick() swm_start = Swarm(STRATEGY_CONTEXT) swm_start.strategy.data = swm_start.strategy.data.ix[:'2016-03-18'] swm_start.strategy.data.loc[:, 'delta'] = 1.0 swm_start.run_swarm() swm_start.pick() ctx = deepcopy(STRATEGY_CONTEXT) ctx['strategy']['opt_preset'] = Swarm._parse_params(swm_start.last_members_list) swm_next = Swarm(ctx) swm_next.strategy.data = swm_next.strategy.data.ix[:'2016-03-25'] swm_next.strategy.data.loc[:, 'delta'] = 1.0 swm_next.run_swarm() dt = '2016-03-18' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(np.isnan(swm_full.series['delta'].ix[dt]), np.isnan(swm_start.series['delta'].ix[dt])) self.assertEqual(swm_start.last_exposure, swm_full.picked_exposure.sum(axis=1).ix['2016-03-18']) # Updating swm_start (assuming that it was loaded from DB) swm_start._laststate_update(swm_next.strategy.data, swm_next.raw_exposure.sum(axis=1), swm_next.strategy.costs) dt = '2016-03-21' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-22' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-23' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-24' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt]) dt = '2016-03-25' self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt]) self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt]) self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt]) self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
def test_laststate_update(self): def reblance_every_5th(swarm): return pd.Series(swarm.index % 5 == 0, index=swarm.index) STRATEGY_CONTEXT = { 'strategy': { 'class': StrategyMACrossTrail, 'exo_name': './mat/strategy_270225.mat', 'direction': -1, 'opt_params': [ # OptParam(name, default_value, min_value, max_value, step) OptParamArray('Direction', [-1]), OptParam('SlowMAPeriod', 20, 10, 40, 5), OptParam('FastMAPeriod', 2, 5, 20, 5), OptParam('MedianPeriod', 5, 2, 10, 1) ], }, 'swarm': { 'members_count': 1, 'ranking_class': RankerHighestReturns(return_period=1), 'rebalance_time_function': reblance_every_5th } } swm_index = np.array(range(11)) swm_values = np.array([ [ 0., 0.], # 0 [ 1., -1.], [ 2., -2.], [ 3., -3.], [ 4., -4.], [ 5., -5.], # 5 [ 6., -6.], [ 7., -7.], [ 8., -8.], [ 9., -9.], [ 6., -6.], # 10 ]) exo_price = np.array([ 0, 1, 2, 3, 4, 5, # 5 6, 7, 8, 9, 6 # 10 ]) exposure_values = np.array([ [1., -1.], # 0 [1., -1.], [1., -1.], [1., -1.], [1., -1.], [1., -1.], # 5 [1., -1.], [1., -1.], [1., -1.], [1., -1.], [1., -1.], # 10 ]) swm = Swarm(STRATEGY_CONTEXT) swm._swarm = pd.DataFrame(swm_values, swm_index) swm._swarm_inposition = pd.DataFrame(np.ones((11, 2)), swm_index) swm._swarm_exposure = pd.DataFrame(exposure_values, swm_index, dtype=np.float) swm.strategy.data = pd.DataFrame({'exo': pd.Series(exo_price, index=swm_index, dtype=np.float)}) swm.strategy.costs = None swm_res = np.array([ 0., #0 - ignored by default 0., 0., 0., 0., 0., #5 - first rebalance (pick system #0) 0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6 1., 2., 3., 0., #10 - pick another systems (but keep prev system change to next day) ]) expected = pd.DataFrame(swm_res, index=swm_index) swm.pick() self.assertEqual(2, len(swm.rebalance_info)) self.assertEqual(5, swm.rebalance_info[0]['rebalance_date']) self.assertEqual(10, swm.rebalance_info[1]['rebalance_date']) for k, v in expected[0].items(): #print(k) self.assertEqual(k, swm.picked_swarm.index[k]) self.assertEqual(v, swm.picked_swarm[0][k]) self.assertEqual(swm.last_date, 10) self.assertEqual(swm.last_rebalance_date, 10) self.assertEqual(swm.last_exposure, 1) self.assertEqual(swm.last_members_list, [1]) self.assertEqual(True, np.all(swm.picked_equity.values == expected[0].values)) # # DO swarm update with new quotes # swm_index = np.array(range(14)) exo_price = np.array([ 0., # 0 - ignored by default 0., 0., 0., 0., 0., # 5 - first rebalance (pick system #0) 0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6 1., 2., 3., 0., # 10 - pick another systems (but keep prev system change to next day) 10., # Should be added with last exposure 11., 13. ]) swarm_exposure = np.array([ 0., # 0 - ignored by default 0., 0., 0., 0., 0., # 5 - first rebalance (pick system #0) 0., 0., 0., 0., 0., # 10 2., # Should be added with last exposure 2., 2., ]) #swm = Swarm(STRATEGY_CONTEXT) # Little hack swm._last_exoquote = 0.0 # exo_price on #10 # Little hack swm._last_exposure = -1 self.assertEqual(swm.last_date, 10) self.assertEqual(swm.last_rebalance_date, 10) self.assertEqual(swm.last_exposure, -1) self.assertEqual(swm.last_exoquote, 0) self.assertEqual(swm.last_members_list, [1]) exo_df = pd.DataFrame({'exo': exo_price}, index=swm_index) swm._laststate_update(exo_df, pd.Series(swarm_exposure, index=swm_index)) self.assertEqual(swm.last_date, 13) self.assertEqual(swm.last_rebalance_date, 10) self.assertEqual(swm.last_exposure, 2) self.assertEqual(swm.last_exoquote, 13) self.assertEqual(swm.last_members_list, [1]) swm_res = np.array([ 0., # 0 - ignored by default 0., 0., 0., 0., 0., # 5 - first rebalance (pick system #0) 0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6 1., 2., 3., 0., # 10 - pick another systems (but keep prev system change to next day) -10., # Apply delayed rebalance we checked rebalance on #10 but change the position at #11 -8., -4., ]) expected = pd.Series(swm_res, index=swm_index) self.assertEqual(len(swm.picked_equity), len(expected)) for k, v in expected.items(): print(k) self.assertEqual(k, swm.picked_equity.index[k]) self.assertEqual(v, swm.picked_equity.values[k])